Avoiding food waste from restaurant tickets: a big data management tool
Journal of Hospitality and Tourism Technology
ISSN: 1757-9880
Article publication date: 1 February 2024
Issue publication date: 5 March 2024
Abstract
Purpose
This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.
Design/methodology/approach
A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.
Findings
The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.
Research limitations/implications
This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.
Originality/value
The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.
Keywords
Acknowledgements
The authors would like to especially thank Dynameat for providing the data used in this work and for the useful discussions.
Funding: This work was partly supported by the State Research Agency of the Ministry of Science and Innovation with reference code AEI/10.13039/501100011033 and PID2022-140786NB-C31.
Statements and declarations. The authors declare no conflict of interest.
Citation
Gómez-Talal, I., González-Serrano, L., Rojo-Álvarez, J.L. and Talón-Ballestero, P. (2024), "Avoiding food waste from restaurant tickets: a big data management tool", Journal of Hospitality and Tourism Technology, Vol. 15 No. 2, pp. 232-253. https://doi.org/10.1108/JHTT-01-2023-0012
Publisher
:Emerald Publishing Limited
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